What to know about LLM fine-tuning
Large language models (LLM) can do many great things out of the box, such as generating content, summarizing text, and writing code. However, there are limits to what they can do. Depending on their training technique and data regime, LLMs can hit their limits in some applications.
In this case, one solution is to fine-tune the LLM for a new application, purpose, or data distribution. In my latest article on TechTalks, I provide a complete overview to the different nuances of LLM fine-tuning. Having this high-level view can help you make better decisions when using LLMs in your applications and business processes.
Key findings:
LLMs (and machine learning models in general) need to be fine-tuned with new data when the distribution of their observations are different from their training dataset. For example, an LLM trained on English will not perform well on text from another language.
In some cases, you want to repurpose the final layers of the model for a new application (computationally inexpensive). In other cases, you need to fine-tune the entire LLM model (computationally expensive).
Some applications might require unsupervised fine-tuning on raw text (e.g., augmenting a pre-trained LLM on a dataset of medical literature). This kind of data is usually easy to obtain.
Other applications need supervised fine-tuning (SFT) on datasets of prompts and their respective responses (e.g., instruction fine-tuning). This kind of data is hard to obtain and requires manual efforts.
Companies with large financial and technical resources us reinforcement learning from human feedback (RLHF) to further align their LLMs with user intent.
Parameter-efficient fine-tuning (PEFT) is a range of techniques that reduce the costs of fine-tuning LLMs on new data.
In some situations, fine-tuning LLMs is not an option. Examples include applications that deal with constantly changing data and are user-context sensitive.
Read the full article on TechTalks.
For more on large language models: